![]() ![]() From left to right, original image by MIO ITO, Daniel Roe, and Carles Rabada on Unsplash Transferring the reduced palette of Gozha’s flower onto other images. Here's what a reduced palette class could look like: The simple solution to this is just to round up these values to the nearest integer and cast it to np.uint8 type before displaying it. When we call kmn.predict(src), the output values are all floats. We have to be careful as the cluster centers are not np.uint8 type but rather np.float64. The code for this is relatively straightforward: run k-means clustering on all the pixels of an image and the resulting cluster centers are your reduced palette. Palette reduction is the algorithmic selection of a subset of the original palette of an image and mapping of all pixel values in the original palette to the closest values in the reduced palette. This number is usually in the tens of thousands for a small photographic image. What I mean by palette is the set of RGB pixel values in an image. So let’s get some definitions in place before anything else. Palette reduction with k-means clustering Look for the code on a prepared Colab notebook containing everything explained in this article. Get ready to use tools from numpy, scikit-learn and dask. In this article, I’m going to explain how this artificial task of palette transfer can be done and how to take it further. While playing around, I casually thought: “what if we transferred the reduced palette of one image into another?” It’s a seemingly innocuous question that expanded my knowledge of colors and parallel-processing toolbox. Today’s use? Well, if you’re particularly nostalgic about the artistry of low-bit graphics or building something to display on a LED matrix, this could be the use case. since only 8 bits or 16 bits are allocated to the entire color palette (in contrast to the 24-bit RGB that is ubiquitous now). By reducing the palette, you can conform to the limited color palette of PAL or SECAM, etc. This was particularly useful in the old days of video game graphics if an image needed to be rendered in a device with limited memory. ![]() ![]() The number of colors that you want in your reduced palette is the k value in k-means clustering in other words the number of cluster centers in RGB space that your image inhabit. This is achieved by doing k-means clustering on the RGB values of the pixels in an image. I recall -from an image processing class back in college - that you can reduce the number of unique colors in an image down to a few colors while keeping the important ones. For example the L2 distance with the LAB color space is better reflects how the person perceives the color similarity.A few weekends ago, I wanted to brush up on my clustering techniques. Quantized_image = quantized_image.reshape(image.shape)Īlso you may consider using other distances and color spaces. Quantized_image = np.array( for idx in neighbours.astype(int)]) Ret, results, neighbours, dist = knn.findNearest(X_query, 1) Knn.train(X_index, cv2.ml.ROW_SAMPLE, np.arange(len(palette))) ![]() X_query = image.reshape(-1, 3).astype(np.float32) Quantized with some random 4-color palette: So you need for each query (a pixel in your image) find the nearest palette color using some distance. You can consider it as a nearest neighbor problem where: Output = quantize_to_palette(image=input, palette=palette) # return the same image with all colors quantized to the nearest palette item So the end result would look something like the following: import cv2 Given some input picture, I would like to have each pixel in the picture assigned to the closest item in the above palette. My BGR palette would look something like this: palette = [ For example, this might include using a clustering technique where the k is fixed and the centroids are predetermined by the palette such that each pixel gets assigned to the nearest color in the palette.įor example, I would like to quantize a picture to the nearest colors in white, black, blue, green and red. While there are a lot of stack overflow solutions using kmeans, I want to be able to do something similar with a preset palette specifically in opencv. Given some arbitrary input color palette, how can I quantize an image to that given color palette? ![]()
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